Method for segmentation of the head-neck arteries, brain and skull in medical images

ABSTRACT

A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Ser. No. 62/074,223provisionally filed on Nov. 3, 2014, titled SYSTEM AND METHOD FORSEGMENTATION OF MEDICAL IMAGES, to Silbert et al, incorporated herein inits entirety.

FIELD OF THE INVENTION

The present disclosure relates to a system and method for medical imagemanipulation and in particular, such a system and method for segmentingthe brain, the skull and segmentation of the head-neck arteries inmedical images.

BACKGROUND OF THE INVENTION

CT (computed tomography) imaging systems can be used by radiologists toexamine the blood vessels of the head and neck including degree ofstenosis (narrowing) or aneurisms. Radiologists analyze the vessels intwo scenarios. In one scenario each vessel is examined separately,traversing along the vessel and checking for aneurisms or stenosis. Thesecond method is by using a MIP (Maximum Intensity Projection) view ofthe head and examining the overall structure of the blood vessels.

For the first scenario, a method for finding the centerline of eachvessel is needed, while for the second scenario, it is desirable to havea view of the head without the intervening skull so as to get a goodview of the various vessels. To implement the above two scenarios,segmentation algorithms for blood vessels, the brain, and the skull needto be developed. Once these are in place a radiologist can choose todisplay the blood vessels only with the skull and brain effectivelyremoved from the image. The segmentation process as it relates to CTimages involves using computational algorithms to identify parts andsystems within the scanned human body. Segmented items can, for example,be highlighted, labeled or removed from the image.

Identification/segmentation of the head-neck arteries in a CT imageallows a radiologist to examine each of the blood vessels in a panoramicview and to traverse along the vessel showing cross sectional views ofthe vessels that allow accurate measurements of the diameter and crosssectional area of the arteries as well as various aneurism and stenosismeasurements.

It is noted that determination of the blood vessels versus otherunrelated elements in the CT image is generally not a trivial task. Inmany cases parts of the blood vessel are missing from the scan, havenon-uniform CT numbers (Hounsfield numbers) and/or the edges of theblood vessels are not clear. Exemplary reasons for this are:

a) Even if the blood vessel is imaged using an intravenous radiocontrastagent, this material may not be uniformly distributed along the bloodvessel;

b) Partial volume effects, especially near bones;

c) Nearby tissue with similar absorption, mainly various bones that havesimilar HU values;

d) Narrowing, splitting and/or other geometrical properties of vessels;

e) Nearby vessels may appear to meet and merge and then diverge;

g) Various effects may cause a vessel to appear to include loops;

h) Occlusions;

i) Some slices may be scanned before the injection of the contrastagent, while other slices are scanned after the injection;

j) Noise.

Prior art segmentation tools have generally required at least somemanual user input, such as identifying the starting point and endingpoints of the neck arteries, and segmenting each image can thereforerequire a lot of time by skilled professionals, making it impractical toroutinely segment large numbers of medical images.

It is therefore desirable to provide a method for segmentation of thehead-neck arteries that is fully automated.

SUMMARY OF THE INVENTION

The present invention overcomes the deficiencies of the background artby providing a system and method, in at least some embodiments, forautomated segmentation of the head-neck arteries, the brain and theskull in CT images.

According to at least some embodiments of the present invention, thereis provided a method for automated segmentation of a segment of a bloodvessel of a subject in a medical image comprising volumetric data,comprising: segmenting circular components in parallel planes of thevolumetric data; and for each of the circular components, identifyingcorresponding circular components from the circular components inadjacent planes to define a contiguous segment of corresponding circularcomponents spanning a plurality of the planes; wherein the contiguoussegment defines a segment of a blood vessel.

Preferably the corresponding circular components are defined by amaximal overlap of the circular components. Alternatively thecorresponding circular components are defined by closeness of the centerof mass of the circular components. Preferably the medical image is a CTscan and the circular components are identified from the intersection ofa wide HU range and a narrow HU range.

According to a further embodiment of the present invention, there isprovided a method for automated segmentation of a blood vessel of a headand neck of a subject in a medical image, the method comprising:identifying the location of anatomical landmarks in the medical image;identifying regions of interest in the medical image based on thelandmarks; segmenting segments of blood vessels in the medical image;classifying at least one of the segments as defining the blood vesselbased on its position relative to the landmarks within the regions ofinterest to create a classified blood vessel; identifying a startingseed for the blood vessel from the classified blood vessel; identifyingan ending seed for the blood vessel from the classified blood vessel;segmenting the blood vessel between the starting seed and the endingseed; and defining a path between the starting seed and the ending seed.

Preferably the blood vessel comprises the right internal and commoncarotid artery, the left internal and common carotid artery, the rightexternal carotid artery, or the left external carotid artery.

Preferably the blood vessel comprises the right vertebral artery or theleft vertebral artery wherein the region of interest comprises asegmented left and right internal carotid artery and segmented basilarartery. Preferably the region of interest is defined based on therelative position of the segmented carotid artery in each slice wherethe segmented carotid artery is located and in the slices where thebrain is segmented.

Preferably the blood vessel comprises at least one of the basilarartery, the aortic arch, or a blood vessel in the brain.

Preferably the image comprises a plurality of slices, the method furthercomprising: automatically identifying a plurality of landmark slicescontaining each of the anatomical landmarks in the medical image,wherein the landmarks comprise at least one of the lungs, trachea,brain, skull, or segmented blood vessels; automatically identifyingrelevant landmark slices for finding the blood vessel based on thepositional relationships of the landmarks and the blood vessel; andmanually identifying a starting seed for the blood vessel from within aset of slices that is constrained to the relevant landmark slices if theidentifying a starting seed fails.

Preferably the method further comprises identifying the skullorientation. Preferably, the medical image comprises volumetric data.

Preferably segmenting segments of blood vessels comprises: segmentingcircular components in parallel planes of the volumetric data; and foreach of the circular components, identifying corresponding circularcomponents from the circular components in the adjacent planes to definea contiguous segment of corresponding circular components spanning aplurality of the planes; wherein the contiguous segment defines asegment of a blood vessel. Preferably the corresponding circularcomponents are defined by a maximal overlap of the circular components.Alternatively, corresponding circular components are defined bycloseness of the center of mass of the circular components. Preferablythe medical image is a CT scan and the circular components areidentified from the intersection of a wide HU range and a narrow HUrange.

Preferably the medical image comprises a plurality of slices. Preferablythe identifying of the anatomical landmarks further comprises: initialsegmentation of the lungs comprising locating the upper lung slice;initial segmentation of the brain comprising locating the brain baseslice; segmentation of the skull comprising locating the skull baseslice; and initial segmentation the trachea further comprisingdetermining the center of the neck on the axial plane, wherein thebottom of the trachea is in the upper lung slice.

Preferably the regions of interest comprise at least one of: the regionbetween the upper lung slice and the brain base slice; the brain, theregion above the skull base slice, or the region between the upper lungslice and the skull base slice. Preferably, the blood vessel is definedas being on the right or the left of the subject based on at least oneof the center of the neck, the center of the image, or the skullorientation.

Preferably the image is obtained after injection of an intravenousradiocontrast agent into the subject.

According to a further embodiment of the present invention, there isprovided a method for semi-automated identification of a seed forsegmentation of a blood vessel of a head and neck of a subject in amedical image wherein the image comprises a plurality of slices, themethod comprising: automatically identifying anatomical landmarks andthe plurality of landmark slices containing each of the anatomicallandmarks in the medical image; automatically identifying relevantlandmark slices for finding the blood vessel based on the positionalrelationships of the landmarks and the blood vessel; and manuallyidentifying the seed for the blood vessel from within a set of slicesthat is constrained to the relevant landmark slices. Preferably thelandmarks comprise at least one of the lungs, trachea, brain, skull, orsegmented blood vessels.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of thepreferred embodiments of the present invention only, and are presentedin order to provide what is believed to be the most useful and readilyunderstood description of the principles and conceptual aspects of theinvention. In this regard, no attempt is made to show structural detailsof the invention in more detail than is necessary for a fundamentalunderstanding of the invention, the description taken with the drawingsmaking apparent to those skilled in the art how the several forms of theinvention may be embodied in practice.

FIG. 1 is a flowchart of an exemplary, illustrative method for brainsegmentation and head-neck artery segmentation in a CT scan according toat least some embodiments of the present invention.

FIG. 2a is a flowchart of an exemplary, illustrative method forsegmenting anatomical landmarks in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 2b is a flowchart of an exemplary, illustrative method for lungsegmentation in a head-neck CT scan according to at least someembodiments of the present invention.

FIG. 2c is a flowchart of an exemplary, illustrative method for initialsegmentation of the brain in a head-neck CT scan according to at leastsome embodiments of the present invention.

FIG. 2d is a flowchart of an exemplary, illustrative method for initialsegmentation of the aorta in a head-neck CT scan according to at leastsome embodiments of the present invention.

FIG. 2e is a flowchart of an exemplary, illustrative method for initialtrachea segmentation in a head-neck CT scan according to at least someembodiments of the present invention.

FIG. 3a is a flowchart of an exemplary, illustrative method for initialsegmentation of the main neck arteries (the common, internal andexternal carotid arteries, and the vertebral arteries) in a head-neck CTscan according to at least some embodiments of the present invention.

FIG. 3b is a flowchart of an exemplary, illustrative method for learningthe artery HU range in a head-neck CT scan according to at least someembodiments of the present invention.

FIG. 3c is a flowchart of an exemplary, illustrative method forfiltering out the vertebral arteries, veins and small vessels in ahead-neck CT scan according to at least some embodiments of the presentinvention.

FIG. 3d is a flowchart of an exemplary, illustrative method for a useraided recovery process according to at least some embodiments of thepresent invention.

FIG. 4 is a flowchart of an exemplary, illustrative method for initialsegmentation of brain arteries in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 5 is a flowchart of an exemplary, illustrative method for skullsegmentation and determining skull orientation in a head-neck CT scanaccording to at least some embodiments of the present invention.

FIG. 6a is a flowchart of an exemplary, illustrative method forcommon-carotid and internal carotid arteries segmentation in a head-neckCT scan according to at least some embodiments of the present invention.

FIG. 6b is a flowchart of an exemplary, illustrative method for seedsselection for the carotid arteries in a head-neck CT scan according toat least some embodiments of the present invention.

FIG. 6c is a flowchart of an exemplary, illustrative method for initialcarotid arteries segmentation in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 6d is a flowchart of an exemplary, illustrative method for refinedcarotid arteries segmentation in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 6e is a flowchart of an exemplary, illustrative method for imagesmoothing in a head-neck CT scan according to at least some embodimentsof the present invention.

FIG. 6f is a flowchart of an exemplary, illustrative method for finalcarotid arteries segmentation in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 6g is a chart of an exemplary slice weight as a function of theslice location to be used in the voxel cost function in a head-neck CTscan according to at least some embodiments of the present invention.

FIG. 6h is a flowchart of an exemplary, illustrative method for a useraided recovery process according to at least some embodiments of thepresent invention.

FIG. 7a is a flowchart of an exemplary, illustrative method forvertebral arteries segmentation in a head-neck CT scan according to atleast some embodiments of the present invention.

FIG. 7b is a flowchart of an exemplary, illustrative method forconstruction of vertebral arteries region of interest, and HU rangeextraction in a head-neck CT scan according to at least some embodimentsof the present invention.

FIG. 7c is a flowchart of an exemplary, illustrative method for basilarartery segmentation in a head-neck CT scan, according to at least someembodiments of the present invention.

FIG. 7d is a flowchart of an exemplary, illustrative method for initialsegmentation of the vertebral arteries, in a head-neck CT scan accordingto at least some embodiments of the present invention.

FIG. 7e is a flowchart of an exemplary, illustrative method for refinedsegmentation of the vertebral arteries, in a head-neck CT scan accordingto at least some embodiments of the present invention.

FIG. 7f is a flowchart of an exemplary, illustrative method for useraided recovery for the basilar artery segmentation and seed selectionaccording to at least some embodiments of the present invention.

FIG. 7g is a flowchart of an exemplary, illustrative method for useraided recovery for the vertebral arteries segmentation and seedselection according to at least some embodiments of the presentinvention.

FIG. 8 is a flowchart of an exemplary, illustrative method for theexternal carotid arteries segmentation according to at least someembodiments of the present invention.

FIG. 9 is a flowchart of an exemplary, illustrative method for aorta arcsegmentation, in a head-neck CT scan according to at least someembodiments of the present invention.

FIG. 10a is a flowchart of an exemplary, illustrative method for skullremoval in a CT scan according to at least some embodiments of thepresent invention.

FIG. 10b is a flowchart of an exemplary, illustrative method forrefining the brain segmentation in a CT scan according to at least someembodiments of the present invention.

FIG. 10c is a flowchart of an exemplary, illustrative method for removalof bone fragments near the carotid arteries in the brain in a CT scanaccording to at least some embodiments of the present invention.

FIG. 10d is a flowchart of an exemplary, illustrative method for addingvessels to the brain in a CT scan according to at least some embodimentsof the present invention.

FIG. 11 is an exemplary, illustrative screenshot of a medical imageviewed in a medical image viewer showing segmented arteries of the headand neck according to at least some embodiments of the presentinvention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention provides a system and method, in at least someembodiments, for manipulation of head-neck CT scan images toautomatically segment the brain and the head-neck arteries preferablyincluding the main head-neck arteries: the common, internal and externalcarotid arteries, the vertebral arteries on the left and right side, theaortic arch, and the basilar artery. Optionally, a user aided recoveryprocess is provided in case the automated method fails that allows theautomated method to continue after some manual input. The algorithms andmethods described are optimized to enable automated segmentation in aminimum amount of time.

The resulting segmentation preferably enables display and labeling on amedical image viewer of the head-neck arteries and further preferablyenables removal from the displayed image of the brain, skull, vertebralbones and all other bones in the image along with any other non-brainsoft tissue, air cavities and any other parts of the image that do notconstitute part of the desired segmented arteries such as unsegmentedblood vessels. Other arteries of the head may optimally be segmented anddisplayed as part of the method.

The method described below is preferably performed in an iterativemanner and includes multiple stages of preliminary identification andsegmentation of anatomical landmarks and points of interest followed byrefinements of these, which may include analysis, marking, repetition ordeletion, until the desired segmentation has been accomplished.Optionally, the methods described are based on partial segmentationsthat have already been performed.

Within these stages and throughout the description below, the proposedvalues used to accomplish these identifications, segmentations andrefinements are those found most useful by the inventors and should notbe considered limiting.

The methods and algorithms described herein preferably use thevolumetric data set from the image or medical image or scan or CT scan(terms used interchangeably) data, where the volumetric data setincludes voxel values that are in Hounsfield units. The Hounsfield units(HU) system is a linear transformation scale from the attenuationcoefficient to a scale where air has the value −1000 and water is zero.This scale in CT images has the value range −1000 (air)-3000 (densebones).

While CT scans generally comprise axial slices of voxels, the volumetricdata may be “sliced” into axial, sagittal or coronal planes depending onthe spatial positioning of the blood vessel that is being segmented.Operation may be carried out in two-dimensional (2d) orthree-dimensional (3d) planes or slices.

The term “component” as used herein refers to a group of voxels whereeach voxel can be reached from any other voxel in the group by travelingalong adjacent voxels. These may be in one specific plane or may spanseveral contiguous planes. These components may be classified as part ofthe algorithm and may represent any anatomical feature captured in thescan including segments of blood vessels, or parts of anatomicallandmarks.

The terms “Dilate”, “Erode”, “Opening”, “Closing” and “StructureElement” as used herein refer to the standard morphological terms as isknown in the art.

The term “Fill Cavity” as used herein refers to the process of addingall voxels that are completely surrounded by segmented voxels. Theoperation is carried out in a two dimensional plane.

The image manipulation and algorithm described here is preferablyperformed on head-neck CT scans of a patient injected with anintravenous radiocontrast agent. Head-neck CT images preferably start atthe upper part of the chest and end at the top of the head.Alternatively, the methods may be performed on a CT of the head onlyscan as will be described below. Optionally, the methods may be used onother types of medical images. Optionally, the methods may be applied tosegmentation of blood vessels, including both arteries and veins, in anyanatomical area of the body. Optionally, the subject of the medicalimage may be non-human.

The segmentation is preferably completely automated and involves thefollowing general stages which are described in extensive detail withreference to the figures below:

Anatomical landmarks—such as the lungs, brain, skull, or blood vesselsare located in the image. These are further refined to find the upperlung slice; the brain base slice; the skull base slice; and the tracheato determine the center of the neck on the axial plane;

Regions of interest are defined based on these anatomical landmarksincluding: the region between the upper lung slice and the brain baseslice; the brain, the region above the skull base slice, or the regionbetween the upper lung slice and the skull base slice. Additionalregions of interest are preferably defined based on segmented arteriesin order to segment other arteries;

Segments of blood vessels are segmented. This process preferablyincludes segmentation of circular components in parallel planes of thevolumetric data making up the image. Contiguous circular components arethen found in adjacent planes, either based on overlapping of thecircular components or based on the closeness of the center of mass ofthese components. These contiguous components define the blood vessels.The vessel segmentation makes use of both narrow and wide HU ranges asdescribed below;

The segmented blood vessels are preferably classified based on theirlocation in a region of interest relative to the landmarks describedabove. The main blood vessels classified and segmented by the process asdescribed herein preferably include the right and left internal,external and common carotid arteries, the right and left vertebralarteries, the basilar artery, the aortic arch, and other blood vesselsin the brain. Preferably, the classification of blood vessels as beingon the right or left is based on the neck centerline as described above,or the center of the image, or the skull orientation or a combination ofthese. Optionally, the method may be used to segment other blood vesselsfrom other parts of the body;

Starting and ending seeds are preferably chosen from the classifiedblood vessels allowing final segmentation of the blood vessel includingall of its segments and allowing definition of the vessel path usingpath mapping tools as described herein.

Optionally, should part of the automated process fail, a semi-automatedprocess is used to recover the automated process. The semi-automatedprocess requires user input to locate blood vessel seeds that were notlocated automatically. The user is preferably assisted and given achoice of slices that is limited to the most relevant slices based onthe landmarks as described above. The semi-automated process istherefore optimized to allow the fastest, most effective manual input.

Referring now to the drawings, FIG. 1 is a flowchart of an exemplary,illustrative method for brain segmentation and head-neck arterysegmentation in a CT scan according to at least some embodiments of thepresent invention. FIG. 1 shows the primary stages of the method, eachof which is described with reference to the figures that follow. Asshown in Stage 1 the main anatomical landmarks in the CT scan aresegmented for use in later stages. The main anatomical landmarkspreferably include the brain, the lungs and the trachea. In additionafter segmenting the carotid arteries, these will serve as anatomicallandmarks for the segmentation of the vertebral arteries. In addition,the CT bed on which the patient is lying is also segmented in order todistinguish it from the patient's body. Stage 1 is further describedwith reference to FIGS. 2a-2e below.

Before Stage 2 commences, it is determined whether the scan is ahead-neck scan or a head scan only. If the scan is a head-neck scan thenthe method proceeds to Stage 2. If the scan is a head scan then themethod proceeds to Stage 9.

In Stage 2 an initial segmentation of the neck arteries is performed.Stage 2 is further described with reference to FIGS. 3a-3c below.

In Stage 3 an initial segmentation of the brain arteries is performed.Stage 3 is further described with reference to FIG. 4 below.

Stage 4 includes skull segmentation and a determination of the skullorientation. Stage 4 is further described with reference to FIG. 5below.

In Stage 5 the segmentation of the common carotid and internal carotidarteries is performed. Stage 5 is further described with reference toFIGS. 6a-6g below. In Stage 5 the start and end seeds for the right andleft internal carotid arteries are selected. Seeds in this context referto voxels in the scanned image that match specific criteria that placesthem with a high degree of certainty in an area representing the bodysegment that needs to be identified. The seeds chosen here represent themost likely start and end points of the arteries in the CT scan. Thestarting seed is the lowest voxel in the common carotid volume, whilethe ending seed is chosen, from various candidates in the brain; to havethe lowest normalized path cost as is known in the art.

In Stage 5 the right and left internal carotid arteries are segmented byfeeding the seeds to a thin vessel segmentation algorithm as in known inthe art such as U.S. Pat. No. 8,229,186 filed Dec. 26, 2004, the entiredisclosure of which is hereby incorporated by reference and for allpurposes in its entirety as if fully set forth herein. The algorithmuses improved local path cost that takes into account calculations fromprior stages. The segmentation of the internal carotid also includes thesegmentation of the common carotid in the neck since the starting seedis the lowest voxel in the common carotid volume.

In Stage 6 the vertebral arteries are segmented. This is preferably doneusing the standard thin-vessel tool segmentation algorithm as referencedabove. The input seeds are set automatically by identifying parts of thevertebral arteries and the basilar artery, and the local cost ismodified using initial segmentation and using some anatomic knowledge,incorporating knowledge from the already segmented carotid arteries.Stage 6 is further described with reference to FIGS. 7a-7e below.

In Stage 7 the external carotid arteries are segmented. This is done byexpanding the initial artery segmentation, selecting starting seedsusing the seed in the common carotid, and an ending seed in the expandedartery segmentation, and segmenting the vessel passing between theseseeds. Stage 7 is further described with reference to FIG. 8 below.

In Stage 8 a segmentation of the aorta arc is performed. Stage 8 isfurther described with reference to FIG. 9 below.

In Stage 9 the skull bones and any other voxels outside the brain aresegmented, excluding all the arteries that were segmented in theprevious stages. Stage 9 is further described with reference to FIGS.10a-10d below.

Reference is now made to FIG. 2a which is a flowchart of an exemplary,illustrative method for segmenting anatomical landmarks in a head-neckCT scan according to at least some embodiments of the present invention.The process shown in FIG. 2a results in initial segmentation of variouselements in the scan, together with the identification of several pointsthat will be used in later stages. As noted before, both for this figureand throughout the specification, proposed values are those found mostuseful by the inventors and should not be considered limiting. Stages2-5 deal with segmentations of anatomical landmarks that are used inlater stages of the algorithm while Stage 1 segments the bed.

In Stage 1, the bed, on which the patient is lying, is segmented andremoved from the scan. The removal of the bed from the scan is doneusing a standard bed segmentation tool as is known in the art.

In Stage 2 the lungs and the uppermost slice of the scan containing thelungs is located. This upper lung slice will be used in later stages tolocate the bottom of the trachea. Since only head-neck scans areconsidered here, only the lower third of the scan is taken into account.An optional method that has been found to be useful by the inventors forlocating the upper slice of the lung is described below with referenceto FIG. 2b Stages 2 a-2 e, however other methods known in the art mayalso be suitable.

An initial segmentation of the brain is needed for the algorithm toidentify the location of the head blood vessels. The brain segmentationmakes use of the brain's unique Hounsfield value range (roughly 0 to 100Hounsfield) and its size. An optional method for the initialsegmentation that has been found to be useful by the inventors isdescribed below with reference to FIG. 2c Stages 3 a-3 j, however othermethods known in the art may also be suitable.

Similarly, an initial segmentation of the aorta is performed in Stage 4.An optional method for the initial segmentation that has been found tobe useful by the inventors is described below with reference to FIG. 2dStages 4 a-4 i, however other methods known in the art may also besuitable.

The initial segmentation of the trachea is described in Stage 5 which isfurther described with reference to FIG. 2e , Stages 5 a-5 e. Thesegmentation is only partial, since the trachea position is used only todetermine the central location of the neck. Vessels on its right areregarded as being on the right side, and vessels on its left areregarded as being on the left side. Here too, an optional method for thepartial segmentation that has been found to be useful by the inventorsis described below with reference to FIG. 2e , Stages 5 a-5 e, howeverother methods known in the art may also be suitable.

Reference is now made to FIG. 2b which is a flowchart of an exemplary,illustrative method for lung segmentation in head-neck CT scan accordingto at least some embodiments of the present invention. FIG. 2b describesStage 2 of FIG. 2a in more detail. In Stage 2 a three sub-images of thelower third of the scan are defined based on threshold Hounsfieldvalues:

a. Non-air tissue having a Hounsfield value above −200.

b. Air/lungs having a Hounsfield value range of −900 to −200-“tissue b”.

c. Padding being out of the Hounsfield range—“tissue c”.

Padding blocks are added to an image in places where the HU in theseplaces is not available, either because the reconstruction was notperformed at this place, or was excluded by the technician.

In Stage 2 b padding voxels which are up to 1 cm in distance from theinitial lung segmentation (tissue b), and are connected to the lungsegmentation through other padding voxels (tissue c), are selected.

In Stage 2 c the padding blocks from Stage 2 b are added to the non-airtissue identified in Stage 2 a. Therefore in scans of a limited field ofview around the lungs, where the surrounding tissue of the lungs isoften not part of the image, this tissue will now be continued by thepadding voxels. Filling the cavity in that tissue and taking itsintersection with the lung tissue (Stage 2 a) will segment the core ofthe lungs.

In Stage 2 d the core of the lungs (result of segmentation of Stage 2 c)is eroded with a 1.5 mm size structuring element in order to disconnectthe airways from the lungs. Large lung components with volumes greaterthan 20 cm³ are kept.

In Stage 2 e the eroded voxels of Stage 2 d are dilated back, with thesame structure element, and the resulting segmentation is defined to bethe lungs. The upper slice is defined to be the upper lung slice. Thelung slice range is thus from the bottom of the data to the upper lungslice.

Reference is now made to FIG. 2c which is a flowchart of an exemplary,illustrative method for initial segmentation of the brain in a head-neckCT scan according to at least some embodiments of the present invention.

In Stage 3 a brain tissue is identified by voxels having a thresholdbetween the Hounsfield range of 0 to 100.

In Stage 3 b small discontinuities in the brain tissue are closed bydilating and then eroding by a 1.5 mm size structure element.

In Stage 3 c the spinal cord is disconnected by eroding using a 2 cmstructure element and then dilating back using the same structureelement.

In Stage 3 d the top-most component with a volume larger than 750 cm³ isselected. If no component is found, it is assumed that the scan endsabove the forehead. In this case, at Stage 3 e the highest component ischosen with a minimal axial cross section of 50 cm². If no suchcomponent exists, the algorithm terminates without results. In Stage 3f, the lowest slice in the selected component is regarded as the skullbase slice and is used in later stages of the algorithm.

In Stage 3 g the component segmented is dilated into the tissuesegmented in Stage 3 b using a 3 cm structure element, to add theTruncus Encephali (Brainstem). In Stage 3 h small vessel holes areclosed by dilating and then eroding with a 3 mm structure element.

In Stage 3 i bone fragments inside the tissue are removed by selectingcomponents with Hounsfield value above 550 and subtracting them from thetissue.

In Stage 3 j voxels in a 3 mm band near air cavities are removed. Theseare selected based on an HU less than or equal to −200. The result isthe brain segmentation, and its lowest slice is regarded as the brainbase slice.

Reference is now made to FIG. 2d which is a flowchart of an exemplary,illustrative method for initial segmentation of the aorta in a head-neckCT scan according to at least some embodiments of the present invention.

In Stage 4 a the slice range identified as the lungs slice range asdescribed above is thresholded between HU values of 100-525. In Stage 4b noise is removed by closing with a 2 voxel size structure element andin Stage 4 c small vessels are eliminated by opening with an 8 mm sizestructure element. Contamination of the image with bone marrow voxels iscorrected by identifying those voxels in Stages 4 d-4 f. In Stage 4 dthe entire image is thresholded for HU values above 525 to select bonemarrow. In Stage 4 e the selection of Stage 4 d is closed with a 12 mmstructure element and in Stage 4 f the resulting voxels are removed fromthe segmentation obtained in Stage 4 c. In Stage 4 g components thattouch the top slice of the lungs are removed. In Stage 4 h the aorta isidentified by selecting the largest component and in Stage 4 i thiscomponent is dilated into the tissue of Stage 4 a with a 3 mm structureelement.

Reference is now made to FIG. 2e which is a flowchart of an exemplary,illustrative method for initial trachea segmentation in a head-neck CTscan according to at least some embodiments of the present invention.

The segmentation of the trachea is started in Stage 5 a by firstthresholding soft tissue in an HU range of −600 to 100. In Stage 5 bthis is then closed with a 10 mm size structure element to include thetrachea and in Stage 5 c eroded with a 15 mm size structure element.

In Stage 5 d the air in the previous segmentation is segmented byselecting all voxels with a Hounsfield value below −600 located in theslices between the upper lung slice and the brain base slice bothidentified above.

In Stage 5 e, to find the center of the neck, an average of the x-axisand y values of the voxels in the trachea segmentation of Stage 5 d ismade. This average value is regarded as the middle of the neck,separating right from left.

Reference is now made to FIG. 3a which is a flowchart of an exemplary,illustrative method for initial segmentation of the Common Carotidarteries in a head-neck CT scan according to at least some embodimentsof the present invention. An initial segmentation of the arteries in theneck is performed. The segmentation exploits the fact that the arterieshave a circular cross section. While bone fragments might have acircular shape in some places for a narrow Hounsfield window range, thearteries have the circular shape both for a narrow and a wide Hounsfieldrange. The results provide candidate arteries from which the actualartery segmentations can be built without having to smooth the image oruse atlas based methods.

In Stage 1 the image is thresholded for soft and hard tissues with HUvalues above 150. In Stage 2 circular components on the axial plane areselected in the slice range between the upper lung slice and the brainbase slice. The circular shape is determined using the ratio of the 2dboundary component length squared, to the area. The ratio is allowed todeviate from 4π by 30% (regarded here as circle tolerance). Allcomponents are required to have an area larger than 1.77 mm² and smallerthan 314 mm².

Out of the above components only voxels with Hounsfield value below 525are selected in Stage 3, and then filtered again in Stage 4 for circleswith circle tolerance of 30% and area in the range of 1.77 mm² to 314mm². If the resulting segmentation is empty, the automatic procedure isstopped and the algorithm continues with a user aided recovery mode asdescribed with reference to FIG. 3d , otherwise it continues to Stage 5.

In Stage 5 the circles are expanded by two voxels on the z-plane.Components with at least 5 mm length in the z-direction are the initialguess for the neck arteries. In these blood vessel components onlyvoxels in the HU range of 150-525 are kept in Stage 6

In order to refine the segmentation, the Hounsfield range for thecarotid arteries is determined from the initial segmentation in Stage 7.This Hounsfield range separates the veins from the arteries. It isassumed that, on average, the veins have lower Hounsfield values thanthe arteries, except the vein where the contrast agent is injected whichhas Hounsfield value above 525. Stage 7 is further described withreference to FIG. 3b below.

In Stage 8, Stages 1 to 4 of FIG. 3a are repeated for the brain slicerange determined in the initial brain segmentation above. In Stage 9candidates for the common carotid component are chosen by onlyconsidering components with a volume larger than 7 mm³ which have aheight (in the z direction) of more than 25 mm (to avoid confusion withthe vertebral arteries, which are usually identified between thevertebrae, and therefore tend to have a height less than 2.5 cm).

In Stage 10 the retained blood vessel components are classified as rightand left blood vessel components using the trachea segmentation that hasbeen done before. These blood vessel components have to lie, at leastpartially, on the bottom third of the neck and therefore blood vesselcomponents that are located in the lower third of the neck are kept inStage 11. The neck is assumed to be, roughly, between the upper lungslice to the brain base slice.

In Stage 12 vertebral arteries, veins and small vessels are filtered outas further described below with reference to FIG. 3c . Finally in Stage13 the lowest blood vessel component on each side is marked as thecommon carotid and a seed is marked on its bottom. If there are nocomponents the algorithm recovers by prompting the user to manuallyidentify the seeds inside the left and right common carotid arteries(FIG. 3d ). After processing of these inputs, the automated process cancontinue with Stage 3 in FIG. 1.

Reference is now made to FIG. 3b which is a flowchart of an exemplary,illustrative method for learning the artery HU range in a head-neck CTscan according to at least some embodiments of the present invention.

In Stage 7 a, a histogram is filled with the Hounsfield value of thevoxels belonging to the initial segmentation.

In Stages 7 b and 7 c, three Hounsfield values are determined. Thevalues v₁ and v₃ are determined using the Otsu's two-level thresholdmethod, as is known in the art in Stage 7 b. The value v₂ is theHounsfield value of the bin with the lowest weight in the range [v₁, v₃]and is determined in Stage 7 c.

The Hounsfield value interval [v₂, v₃] is assumed to be controlled bythe arteries, with little contamination from veins. However, this rangeis not exact and has to be refined. This is done in Stage 7 d. In casethere exists a local minimum in the histogram above v₃, it is assumedthat this minimum indicates the interface between veins and arteries,and v₂ is relocated to that minima. In any case, v₃ is then relocated tothe bin with the highest Hounsfield value above v₂ with a weight of atleast 10% of the highest weight bin above v₂.

Since the Hounsfield distribution of the veins and the arteries mayoverlap, the interval [v₂, v₃] is only a partial interval for thearteries' Hounsfield range. In Stage 7 e the entire range is determinedby fitting a Gaussian template to the histogram in the range [v₂, v₃].In Stage 7 f, the artery range is then defined to have its highestHounsfield value at the mean of the Gaussian plus two standarddeviations, and its minimum value at the mean of the Gaussian minus twostandard deviations, but cannot be smaller than 150 Hounsfield units.

FIG. 3c is a flowchart of an exemplary, illustrative method forfiltering out the vertebral arteries, veins and small vessels in ahead-neck CT scan according to at least some embodiments of the presentinvention.

In Stage 12 a the blood vessel components which are not central aredisregarded. This is done by iterating over all pairs of blood vesselcomponents, at each side separately. For each pair, in case the bloodvessel components have more than 10% of their size overlap in height(z-axis) and in the forward-backward direction (y-axis) the more centralin the right-left direction (closest to the trachea in the x-axis) iskept, while the other component is disregarded. This Stage is veryefficient in rejecting veins since veins are usually external to thecarotids in the neck.

In Stage 12 b only forward blood vessel components are kept. This isdone by iterating over all pairs of the blood vessel componentsremaining after Stage 12 a. For each pair of blood vessel componentsthat have an overlap of more than 10% in the z-direction the followingaspects are checked: if at least half the length in the z-direction ofone of the blood vessel components is both behind (in the y-direction)and has a cross-sectional area of less than half the size of the otherblood vessel component then it is disregarded. This Stage is veryefficient in rejecting the vertebral arteries since the vertebralarteries are generally thinner and tend to lie in the back of the neck.

In Stage 12 c the common carotid component is regarded as the bloodvessel component with the lowest slice on each side from the remainingblood vessel components in Stage 12 b. In Stage 12 d the center of massof the lowest slice of each of the common carotid candidates is chosenas a seed for later use.

Reference is now made to FIG. 3d which is a flowchart of an exemplary,illustrative method for a user aided recovery process for the commoncarotid segmentation and seed selection. After the recovery process endsthe automatic procedure continues in Stage 3 of FIG. 1. In Stage A theuser is prompted to enter seeds in the common carotid. If no commoncarotids were found at all, the user is prompted for a seed input forboth common carotids at a position of 2 cm above the lung slice. In caseone common carotid was found, the user is prompted for a seed in theother common carotid at the same slice where the first common carotidseed was found.

In Stage B the input seeds are ballooned using a fast marching tool witha local cost of 1 in the HU range between 150-525 and exponential costoutside this range at the scale of 20:

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {150 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 150} \\{1,} & {150 < {HU} < 525} \\{{\exp \left\lbrack {{- \left( {{HU} - 525} \right)}/20} \right\rbrack},} & {{HU} > 525}\end{matrix} \right.$

The balloon runs repeatedly for five iterations. The seeds for eachiteration are the result of the previous iteration, and the stoppingcriteria is a path cost of 5.

In Stage C a histogram is filled with the HU distribution of theballooned components.

In Stage D the carotid HU range is extracted from the histogramaccording to the procedure described in FIG. 3b . After this stage theautomatic algorithm is recovered and continues as in FIG. 1 Stage 3.

Reference is now made to FIG. 4 which is a flowchart of an exemplary,illustrative method for initial segmentation of brain arteries in ahead-neck CT scan according to at least some embodiments of the presentinvention. In a later stage, a candidate seed will be selected from eachof these components.

In Stage 1 all voxels above 525 Hounsfield or below −50 Hounsfield areremoved from the initial brain segmentation.

In Stage 2 the remaining brain segmentation is opened using a 10 mm sizestructuring element to disassociate irrelevant parts from the mainvolume.

In Stage 3 only the largest component is retained as the brainsegmentation and only voxels above the skull base slice are considered.

To segment the arteries in Stage 4, only voxels with Hounsfield rangeabove 80 are selected in the slice range between the brain base sliceand the largest cross sectional slice in Stage 3.

In Stage 5, only circle-like components on the sagittal plane areselected, with a circle tolerance of 30% and an area in the range 1.77mm²-314 mm².

In Stage 6, dilation with a 2 voxel size structuring element is done,considering only voxels in the relevant Hounsfield value range above 80.

In Stage 7 only components that are at least 4 mm in size in thex-direction (left to right) are retained. These components are assumedto be fragments of the middle cerebral arteries (MCA) which typicallylie in a left-right orientation with respect to the head. The MCAcomponents are used as the ending seed points for the internal carotidsegmentation.

In Stage 8 voxels with an HU above 525 that do not overlap with thebrain segmentation in Stage 3 are removed.

In Stage 9 the large bone fragments are no longer circle-like and can beremoved as follows: only circle-like components on the sagittal planeare filtered, with a circle tolerance of 30% and an area in the range1.77 mm²−314 mm².

In Stage 10, these are dilated with a 2 voxel size structuring element,considering only voxels in the Hounsfield value range above 80.

In Stage 11 only artery components that are at least 4 mm in size on thex-direction (left to right) are retained.

Reference is now made to FIG. 5 which is a flowchart of an exemplary,illustrative method for skull segmentation and determining skullorientation in a head-neck CT scan according to at least someembodiments of the present invention.

In Stages 1 to 6 the back skull is segmented. In Stage 1 only brainsegmentation above the skull base slice is considered. The result isdilated on the axial plane with 1 cm size structure element in Stage 2.In Stage 3 this is eroded on the axial plane with 1 cm size structureelement. The result of Stage 3 is removed from the result of Stage 2.

In Stage 4 the segmentation is divided into two halves: front andback—and only the back half is kept. In Stage 5 voxels with an HU valuebelow 800 are removed and in Stage 6 the result is dilated with a 5 mmsize structure element to complete the back skull segmentation. Sincethe arteries do not pass near the skull at the back of the head, it isassumed that the result is an artery free zone, and this will be usedlater to restrict the segmentation of the arteries and prevent them fromleaking into the veins that do pass near the back of the skull.

In Stages 7 to 10, the skull orientation is determined. The skullorientation is required in addition to the trachea segmentation sincethe head of the patient may be rotated with respect to the center of theneck. Skull orientation is determined as follows.

In Stage 7 the image is filtered for Hounsfield values above 800 in theslices above the skull base slice. In Stage 8 the segmented bed that thepatient is lying on is removed. In Stage 9, the result is dilated with a5 mm size structure element. In Stage 10, since in most cases the headis not perfectly aligned with the modality axes, a primary componentanalysis (PCA) is used on the largest volume components to deduce theprimary axis and the center of mass location.

The result of Stages 1-4 is an identification of blood vessel candidatesin the neck, initial segmentation of the blood vessels in the head abovethe skull base slice and initial segmentation of the left and rightcommon carotid arteries. The carotid arteries in the general area abovethe brain base slice and below the skull base slice are not detected.These areas are characterized by the carotids traversing adjacent tobones or having convoluted paths and a different Stage of the algorithmis required to segment the carotid arteries there.

Reference is now made to FIG. 6a which is a flowchart of an exemplary,illustrative method for common-carotid and internal carotid arteriessegmentation in a head-neck CT scan according to at least someembodiments of the present invention.

In Stage 1 seeds are selected for the carotid arteries as furtherdescribed below with reference to FIG. 6b . In Stage 2 the initialcarotid arteries segmentation is performed as further described belowwith reference to FIG. 6c . In Stage 3 refined carotid arteriessegmentation is performed as further described below with reference toFIG. 6d . In Stage 4 the image is smoothed as further described belowwith reference to FIG. 6e . In Stage 5 the final carotid arteriessegmentation is performed as further described below with reference toFIG. 6f

The stages shown in FIG. 3a and FIG. 4 result in choices of seeds thatrepresent the most likely start and end points of the internal carotidarteries in the CT scan. For the internal carotid arteries segmentationthere is a need to have two seeds for each vessel. One seed is locatedat the base of the common carotid artery, and the second seed is locatedat the end of the internal carotid artery in the brain. The startingseeds in the common carotid arteries are the lowest voxels in each ofthe initial common carotid artery segmentations derived previously(Stage 6 of FIG. 3a ).

The ending seed is deduced by iterating through vessel candidates in thebrain (which are potential candidates for the end of the internalcarotid arteries and were segmented in FIG. 4) and finding the bestcandidate to serve as a seed. This procedure is done in several stepswhich will be further described below:

1) Fix a seed for each candidate blood vessel segment in the brain

2) Associate a score with each seed as a potential part of the internalcarotid

3) Refine the score using anatomical knowledge

Reference is now made to FIG. 6b which is a flowchart of an exemplary,illustrative method for seeds selection for the carotid arteries in ahead-neck CT scan according to at least some embodiments of the presentinvention.

Stage 1 a starts with a list of potential blood vessel components fromthe brain arteries, as described above with reference to FIG. 4. Fromthis list, components that overlap with the back skull segmentation asdescribed above with reference to FIG. 5 are removed. In Stage 1 b thecomponents are classified into right and left components. Rightcomponents are all components whose voxels all fall between 1 cm to theleft of the neck center and the right of the image. Left components areall components whose voxels all fall between 1 cm to the right of thenext center and the left of the image. By the above definition, acomponent can be considered both a left and right component.

In Stage 1 c two histograms are filled for the HU multiplicitydistribution for the neck and brain arteries. The brain arterieshistogram is filled by dilating the brain arteries segmentation (withreference to FIG. 4) using a 2 mm structure element. The neck arterieshistogram is filled using the neck arteries segmentation as describedwith reference to FIG. 3 a.

In Stage 1 d a score is assigned to each blood vessel component by usingthe Fast marching algorithm such as described in U.S. Pat. No. 8,352,174the entire disclosure of which is hereby incorporated by reference andfor all purposes in its entirety as if fully set forth herein. The scoreof each component is the fast-marching path cost distance normalized bythe Euclidean distance needed to go from a seed in the neck (withreference to FIG. 3a ) to the seed in the component using the twohistograms to construct local costs. The neck histogram is used toconstruct the cost below the brain slice, and the brain histogram isused to construct the cost above the brain slice. The local cost isdefined by taking the inverse weight of the histogram and modifying itin order to take into account prior information. An algorithm tocalculate the local cost for a voxel is given as:

a. The local cost is initially assigned the inverse weight of that voxelin the histogram to which it belongs (brain arteries histogram or neckarteries histogram). That is, the bin to which the voxel contributes istaken according to its HU value and the cost is the inverse bin count ofthat bin.

b. If the voxel is located in the initial segmentation (FIG. 3a Stage13) of the common carotid its local cost is multiplied by 0.1

c. If the voxel is located inside the artery-free zone (FIG. 5 Stages1-6) its weight is multiplied by 10.

d. If the voxel falls inside the general artery-like segmentation (FIG.3a Stage 4), its weight is multiplied by 0.8.

e. If the fast marching is initialized with the seed of the right commoncarotid, and the voxel falls on the left side of the primary axis (FIG.5 Stage 10) it is multiplied by 1+0.16d², where d is the distance in mmof the voxel from the primary axis. For a march initialized with theleft common carotid seed the mirror condition is applied.

In Stage 1 e—Separately for each side iterate all pairs of blood vesselcomponents and order them using the score. Then reduce 5% from the scoreof the component that is in front or above the other for each of thepairs. The component with the lowest score is considered to be the MCAcomponent. A seed is chosen on the far end of the component. If no bloodvessel component is found a user aided recovery procedure is initiatedand the process continues to FIG. 6 h.

Reference is now made to FIG. 6c which is a flowchart of an exemplary,illustrative method for initial carotid arteries segmentation in ahead-neck CT scan according to at least some embodiments of the presentinvention. In Stage 2 a a standard vessel segmentation tool is used tosegment the arteries using the seeds as derived with reference to FIGS.3a and 6b . The local cost is determined in the same way as defined withreference to FIG. 6 b.

Reference is now made to FIG. 6d which is a flowchart of an exemplary,illustrative method for refined carotid arteries segmentation in ahead-neck CT scan according to at least some embodiments of the presentinvention. In Stage 3 a the centerline of the vessel on each side, asderived with reference to FIG. 6c , is dilated with a 1 voxel structureelement. In Stage 3 b the centerline segmentation is used to fill twohistograms for each side. One for the brain above the skull base slice,and one for the neck below the skull base slice.

In Stage 3 c, for each of the histograms fit a Gaussian distributionaround the maximal height, considering only bins that are higher thanhalf of the maximum height. In Stage 3 d the segmentation as performedwith reference to FIG. 6c is repeated with the same seeds but theGaussian model is used for the local cost instead of the histograms sothat the initial cost in step a of the above algorithm is the inverse ofthe histogram.

Reference is now made to FIG. 6e which is a flowchart of an exemplary,illustrative method for image smoothing in a head-neck CT scan accordingto at least some embodiments of the present invention. In Stage 4 a foreach side, a histogram is filled with HU gradient magnitude valuesbelonging to the vessels segmented in Stage 3 d. The gradient iscalculated using a Gaussian filter with a scale of 1 mm. In Stage 4 b agradient threshold is chosen from the histogram to be the 75^(th)percentile. In Stage 4 c the smoothing ROI is set by dilating the rightand left segmented vessels with a 25 mm structure element. The ROI isconstructed in order to speed up the smoothing process, since there isno need to smooth the image far away from the initial segmentation ofthe vessel. In Stage 4 d the image is smoothed using a non-linearPerona-Malik model with an exponential function using the gradientthreshold as the conductance.

Reference is now made to FIG. 6f which is a flowchart of an exemplary,illustrative method for final carotid arteries segmentation in ahead-neck CT scan according to at least some embodiments of the presentinvention. Stages 5 a-5 e result in the calculation of the carotidsiphon segment slice range. In Stage 5 a, for the left and right carotidsegmentations separately, the segmented vessel from Stage 3 d of FIG. 6dis dilated with a 1 cm structure element on the axial plane. In Stage 5b voxels of air with HU values smaller than −700 are selected. In Stage5 c air components with 10 mm on the axial plane and 2 mm in thez-direction are dilated. In Stage 5 d the front most component withvolume smaller than 25 cm³ is selected. In Stage 5 e voxels thatintersect with the segmented vessel from Stage 3 d are selected and theslice range is calculated. This is the slice range of the carotid siphonsegment.

Stages 5 f-5 i result in the calculation of the sinus slice range. InStage 5 f, for the left and right carotid segmentations from Stage 3 dseparately, each segmentation is dilated with a 5 mm structure elementon the axial plane. In Stage 5 g voxels of bone with an HU value above500 are selected. In Stage 5 h the result is closed with a 1 cmstructure element. In Stage 5 i voxels that intersect with the segmentedvessel from Stage 3 d are selected and the slice range is calculated.This is the slice range of the sinuses.

In Stage 5 j, for the left and right segmented carotid arteries fromStage 3 d the vessel centerline is dilated with a 2 mm structureelement. In Stage 5 k a histogram is filled using the dilated centerlinefor the neck (below the sinuses) and for the brain (above the carotidsiphon segment). In Stage 5 l the left and right carotid arteries aresegmented again using the smoothed image with a local cost which isdetermined by the weighted average of the two histograms where theweights are a function of the slice (FIG. 6g ). Having two histogram h₁and h₂ and a weight w, the weighted average is.

wh ₁+(1−w)h ₂

The weight function w is interpolated between slices as follow (s is theslice number, s_(s) is the slice number where the sinus begins, s_(p) isthe slice number where the siphon begins):

${w(s)} = \left\{ \begin{matrix}{1,} & {s < s_{s}} \\{\frac{s_{p} - s}{2\left( {s_{p} - s_{s}} \right)},} & {s_{s} \leq s < s_{p}} \\{0,} & {s > s_{p}}\end{matrix} \right.$

The seeds used are the same seeds that were used previously to segmentthese vessels.

Reference is now made to FIG. 6g which is a chart of an exemplaryillustrative method for interpolated histogram vs. slice location of avoxel in a head-neck CT scan according to at least some embodiments ofthe present invention. The graph shows the weights as a function of theslices that are being used in the averaging which are calculated inStage 5 l.

Reference is now made to FIG. 6h which is a chart of an exemplaryillustrative method for user aided recovery for the MCA segmentation andseed selection.

In Stage A the user is prompted for a seed in the MCA. In case both MCAsare missing the user is prompted for seeds in the slice of 1.5 cm belowthe slice with the largest cross section in the brain segmentation. Incases where one of the MCAs was found the user is prompted for a seed inthe same slice as where the other MCA was found.

In Stage B the input seeds are ballooned using a fast marching tool witha local cost of 1 in the HU range of 100-525 and a cost that growsexponentially outside this rage with a scale of 20:

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {100 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 100} \\{1,} & {100 < {HU} < 525} \\{{\exp \left\lbrack {{- \left( {{HU} - 525} \right)}/20} \right\rbrack},} & {{HU} > 525}\end{matrix} \right.$

The balloon is done in 5 iterations, where the input seeds for eachiteration are the output segmentation of the previous iteration, and thestopping criteria is a path cost of 2. After this stage the automaticprocedure is recovered and the algorithm continues in FIG. 6a Stage 2.

Reference is now made to FIG. 7a which is a flowchart of an exemplary,illustrative method for vertebral arteries segmentation in a head-neckCT scan according to at least some embodiments of the present invention.

In Stage 1 the region of interest of the vertebral arteries isconstructed and the HU range is extracted. Stage 1 is further describedwith reference to FIG. 7b . In Stage 2 a seed for the basilar artery isfound. Stage 2 is further described with reference to FIG. 7c . In Stage3 the initial vertebral arteries segmentation is performed. Stage 3 isfurther described with reference to FIG. 7d . In Stage 4 the refinedvertebral arteries segmentation is performed. Stage 4 is furtherdescribed with reference to FIG. 7 e.

Reference is now made to FIG. 7b which is a flowchart of an exemplary,illustrative method for construction of vertebral arteries region ofinterest (ROI), and HU range extraction in a head-neck CT scan accordingto at least some embodiments of the present invention.

Stages 1 a-1 c define the ROI. Stage 1 a iterates through slices fromthe neck to the brain as defined above. At each slice a circle is fittedthat passes through the centerline of the segmented internal carotidarteries. The arc length between the carotid is fixed to be 120 degrees.The ambiguity for the side of the center is reduced by choosing thecenter to be on the back side of the image. Increasing the circle radiusby 2 cm, to take into account twists along the carotid paths, definesthe vertebral ROI as the set of all such circles. The ROI will be usedlater on to speed up the process of circle finding, since the vertebralarteries pass inside the ROI, and there is no need to look for themelsewhere. In Stage 1 b the internal carotid arteries are removed fromthe ROI and the result is eroded with a 5 mm structure element on theaxial plane to exclude some of the vertebral bones and spinal cord. InStage 1 c the brain segmentation derived above is added to the ROI.

In Stage 1 d a histogram is filled with the HU distribution of theinternal carotid arteries. In Stage 1 e the lowest bin with a valueclosest to half of the peak value is found and a tangent line is fittedto the histogram at that bin value. The place where the tangent linecrosses 10% of the peak value is set to be the low threshold HU value ofthe vertebral arteries. In Stage if the highest bin with a value closestto half of the peak value is set to be the high threshold HU value ofthe vertebral arteries.

Reference is now made to FIG. 7c which is a flowchart of an exemplary,illustrative method for basilar artery segmentation in a head-neck CTscan, according to at least some embodiments of the present invention.

Stages 2 a-2 d aim to derive the lower Hounsfield value of theHounsfield range of the vertebro-basilar system. In Stage 2 a the imageis thresholded in the slice range between the lung slice and the brainslice in the HU range found as described above with reference to FIG. 7b. In Stage 2 b the result is dilated by one voxel. In Stage 2 c theinternal carotid segmentation is removed. In Stage 2 d a histogram isfilled and the HU range is defined as the range having values largerthan half the peak around the peak.

In Stage 2 e the image is thresholded above the lung slice for the HUrange found in Stage 2 d. In Stage 2 f, for each slice a rectangularregion of interest is defined starting 2 cm to the sides of thecarotids, and extending 8 cm behind the carotids. In Stage 2 g theinternal carotids are removed from the thresholded image. In Stage 2 h afilter is applied at the intersection of the results of the previousstage and the ROI defined in FIG. 7b , keeping only voxels in circles onthe axial, sagittal and coronal planes with a cross sectional area inthe range of 1.77-314 mm² and tolerance of 30%. In Stage 2 i the resultis dilated by 2 voxels, considering only voxels in the Hounsfield rangefound in Stage 2 d.

In Stage 2 j components shorter than 1 cm in the z-direction areremoved. In Stage 2 k voxels outside the brain are removed. In Stage 2 la filter is applied keeping only voxels in circles on the axial planewith a cross sectional area in the range of 1.77-314 mm² and toleranceof 30%. In Stage 2 m the result is dilated 5 mm in the z-directionconsidering voxels in the vertebral Hounsfield range (Stage 2 d). InStage 2 n components longer than 1.5 cm in the z-direction are kept. InStage 2 o voxels in the vertebral HU range connected to the componentsfound in Stage 2 n are added.

In Stage 2 p the largest component is kept. This should be longer than 2cm in the z-direction. If no component is found a user aided recoveryprocedure is enabled and the process continues to FIG. 7 f.

In Stage 2 q the algorithm runs through the slices from bottom to topand top to bottom in the component. For each slice only one 2d componentis kept. Optionally, the 2d component which has the maximal overlap withthe 2d component in the previous slice is used. Optionally, the 2dcomponent whose center of mass is closest to the center of mass of theprevious slice is used. Both options provide similar results but thesecond is more time efficient.

In Stage 2 r the resulting blood vessel component is ballooned usingfast marching with local cost of 1 for HU values above 150 and below themaximal vertebral Hounsfield range. The cost is raised exponentially forHU values outside this range with a scale of 20, as in the formula below(H_(up) is the upper Hounsfield value).

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {150 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 150} \\{1,} & {150 < {HU} < H_{up}} \\{{\exp \left\lbrack {{- \left( {{HU} - H_{up}} \right)}/20} \right\rbrack},} & {{HU} > H_{up}}\end{matrix} \right.$

Propagation in the z-direction divides the local cost by 4. The stoppingcriteria is defined as a total path cost of 5.

In Stage 2 s, Stage 2 r is repeated. In Stage 2 t the resultingcomponent is the basilar artery. A seed is fixed at the top of thesegmentation found in Stage 2 q. If no blood vessel component is found auser aided recovery procedure is enabled and the process continues toFIG. 7 f.

Reference is now made to FIG. 7d which is a flowchart of an exemplary,illustrative method for initial segmentation of the vertebral arteries,in a head-neck CT scan according to at least some embodiments of thepresent invention.

In Stage 3 a the image is thresholded for HU values above 150 in theslice range between 1.5 cm below the lung slice and the brain slice andlocated inside the ROI defined with reference to FIG. 7b . In Stage 3 bthe largest component is kept. In Stage 3 c a filter is applied forcircles on the axial plane with a cross sectional area up to 80 mm² andtolerance of 30%. In Stage 3 d voxels with an HU value above 525 areremoved. In Stage 3 e, Stage 3 c is repeated and only those circles arekept. In Stage 3 f the result is ballooned in the same way as in Stage 2r of FIG. 7c but using an HU range of 90-525.

In Stages 3 g-3 m the vertebral arteries are grown through the vertebralbones. In Stage 3 g the input segmentation from Stage 3 f is dilated 5mm in the z direction considering voxels with HU values from 150-525. InStage 3 h the result is ballooned in the same way as done in Stage 3 f.In Stage 3 i blood vessel components that are longer than 4 cm in the zdirection are kept. In Stage 3 j a filter is applied for circles on theaxial plane with a cross sectional area smaller than 80 mm² and 30%tolerance. In Stage 3 k the image is opened once in the z-direction, andthen dilated by 1 mm on the axial plane and 5 mm in the z-direction.

In Stage 3 l, Stages 3 h, 3 i, and 3 j are repeated. In Stage 3 m,Stages 3 h and 3 j are repeated (while skipping Stage 3 i) 5 more times.In Stage 3 n, the result is dilated 3 mm in the z-direction consideringvoxels with an HU value in the range 150-525. In Stage 3 o, componentslonger than 4 cm in the z-direction are kept. Stage 3 p involvesiterating through the slices and keeping 2d components with an areasmaller than 80 mm².

In Stage 3 q, a new image is thresholded for vertebrae bones inside theROI with HU values above 800. In Stage 3 r, the result is dilated 1 cmin the z direction. In Stage 3 s, the longest component in the zdirection is kept. In Stage 3 t the result is dilated 5 mm on the axialplane. In Stage 3 u, only the components of Stage 3 p that intersectwith bone tissue in Stage 3 t are kept. In Stage 3 v, on each slice 2 dcomponents are classified between left and right relative to the centerof the neck.

Reference is now made to FIG. 7e which is a flowchart of an exemplary,illustrative method for refined segmentation of the vertebral arteries,in a head-neck CT scan according to at least some embodiments of thepresent invention.

In Stage 4 a, a starting seed is set on each side as the center of massof the lowest slice from each side found in Stage 3 v. If one bloodvessel component or two are missing after Stage 3 v a user aidedrecovery procedure is enabled and the process continues to FIG. 7g .This stage repeats itself after the recovery process, therefore in caseswhere this stage fails after the recovery process itself has run, thealgorithm terminates with a failure.

In Stage 4 b the basilar seed is set as the ending seed. In Stage 4 c ahistogram is filled with the HU distribution for the vertebral arteriesfrom the Stage 3 FIG. 7a . segmentation. In Stage 4 d the standardvessel tracking tool is used to segment the left and right vertebralarteries using each of left and right seeds respectively as the startseed and the basilar seed as the end seed. The local cost is the inverseof the histogram weight while avoiding marching in the carotid arteriesby giving a cost of infinity to voxels belonging to the carotid arteriespreviously segmented.

Reference is now made to FIG. 7f which is a flowchart of an exemplary,illustrative method for user aided recovery for the basilar arterysegmentation and seed selection.

In Stage A the user is prompted for a seed in the basilar artery. Theuser is prompted for the seed in the middle slice between the skull baseslice and the slice where the seeds of the MCA are located in (if theyare not on the same slice then their average slice number).

In Stage B the seed is ballooned using fast marching with a local costof 1 in the HU range 150-525 and increasing exponentially outside thisrange on a scale of 20 HU:

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {150 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 150} \\{1,} & {150 < {HU} < 525} \\{{\exp \left\lbrack {{- \left( {{HU} - 525} \right)}/20} \right\rbrack},} & {{HU} > 525}\end{matrix} \right.$

The balloon is run in five iterations, where the seeds for eachiteration are the results of the previous one. The stopping criterionfor each iteration is a cost of 5. After this stage the automaticprocedure is recovered and the algorithm continues is FIG. 7a Stage 3.

Reference is now made to FIG. 7g which is a flowchart of an exemplary,illustrative method for user aided recovery for the vertebral arteriessegmentation and seed selection.

In Stage A the user is prompted for seeds in the vertebral arteries. Incases when only one seed is missing the user is prompted to input theother seed on the same slice where the first seed is located. If bothseeds are missing then the user is prompted for a seed on the lowestslice of the ROI (FIG. 7b ). If there is no ROI then the user isprompted for seeds in the slice which is located 2 cm above the top lungslice.

In Stage B the seed is ballooned using fast marching with a local costof 1 in the HU range 150-525 growing exponentially outside this range ona scale of 20 HU:

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {150 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 150} \\{1,} & {150 < {HU} < 525} \\{{\exp \left\lbrack {{- \left( {{HU} - 525} \right)}/20} \right\rbrack},} & {{HU} > 525}\end{matrix} \right.$

The balloon is run in five iterations, where the seeds for eachiteration are the result of the previous one. The stopping criterion foreach iteration is a cost of 5. After this stage the automatic procedureis recovered and the algorithm resumes in FIG. 7e Stage 4 a.

Reference is now made to FIG. 8 which is a flowchart of an exemplary,illustrative method for the external carotid arteries segmentationaccording to at least some embodiments of the present invention.

In Stage 1 the carotid arteries segmentation is inflated below the brainslice using the local cost function of the form shown in FIG. 7c Stage 2r with the carotid HU range as was derived in FIG. 3b Stage 7 f with ascale of 20. The inflation is stopped at a path cost of 25. This stageis repeated 5 times. In Stage 2 the carotid arteries are removed fromthe resulting segmentation. In Stage 3 the right most voxel is selectedas the ending seed for the right external carotid artery, and the leftmost voxel is selected as the ending seed for the left carotid artery.In Stage 4 the external carotid arteries are segmented using a startingseed derived as above with reference to FIG. 3a and ending seeds fromthe previous Stage using a standard vessel tracking tool. The costfunction is derived from a histogram of the HU distribution of thevoxels inside the internal carotid segmentation. The cost function isthe inverse of the bin value.

Reference is now made to FIG. 9 which is a flowchart of an exemplary,illustrative method for aorta arc segmentation, in a head-neck CT scanaccording to at least some embodiments of the present invention.

In Stage 1 a seed is set inside the aorta by iterative erosion of theinitial aorta segmentation (FIG. 2d ) until the last erosion. The lowestvoxel is the seed. In Stage 2 the initial aorta segmentation isballooned in a similar way to the ballooning of FIG. 7c Stage 2 r. InStage 3 circular components on the axial plane with an area in the rangeof 7.08-1256 mm² and 30% tolerance are kept. In Stage 4, Stages 2 and 3are repeated two more times, using the resulting segmentation as thestarting point for the ballooning. In Stage 5, Stage 2 is repeated twomore times, using the resulting segmentation as the starting point forthe ballooning.

In Stage 6 bone marrow is removed by thresholding the image for an HUabove 500. The result is closed with a 12 mm structure element and thesevoxels are removed from the results of Stage 5. Stage 7 requiresiteration through all segmented vessels (The carotid and vertebralarteries). For each vessel the beginning of that vessel is segmented byusing a standard vessel tracking tool from the aorta seed to thestarting seed of the vessel. The local cost is determined by the inverseHU distribution of the vessel. Marching is forbidden inside othervessels, and the local cost of voxels belonging to the segmentation ofStage 6 is multiplied by 0.1.

Reference is now made to FIG. 10a which is a flowchart of an exemplary,illustrative method for skull removal in a CT scan according to at leastsome embodiments of the present invention.

Although, this process is referred to as skull removal or segmentation,the actual process involves the segmentation of all voxels that do notbelong to the brain or the arteries segmented in the previous stages.Thus, the skull, teeth, vertebral bones and all other bones in the imageare segmented, along with any other non-brain soft tissue and aircavities. As described above, these segmented components can be labeled,added or removed from the image viewed in a medical image viewer suchthat a radiologist might view only the significant arteries of the headand neck.

In Stage 1 the brain segmentation is refined. Stage 1 is furtherdescribed with reference to FIG. 10b . In Stage 2 bone fragments nearthe carotid arteries in the brain are removed. Stage 2 is furtherdescribed with reference to FIG. 10c . In Stage 3 vessels are added tobrain. Stage 3 is further described with reference to FIG. 10d . Oncethese stages are complete, all segmentations have been performed andthese can now be displayed or hidden by the user. For example, vesselsmay be highlighted while the brain is hidden, allowing a medicalprofessional to view healthy vessels or aneurisms, occlusions, or othercomplications in the brain vessels. FIG. 11 shows such an exemplary,illustrative screenshot of a medical image viewed in this way followingcompletion of the segmentation algorithm.

Reference is now made to FIG. 10b which is a flowchart of an exemplary,illustrative method for refining the brain segmentation in a CT scanaccording to at least some embodiments of the present invention.

In Stage 1 a the mean and standard deviations of the Hounsfield valuefor the brain from the initial brain segmentation are calculated. InStage 1 b the image above the skull base slice is thresholded for the HUrange of one standard deviation around the mean. In Stage 1 c the resultis eroded 2 mm on the axial plane to disconnect non-brain soft tissue.In Stage 1 d the algorithm goes through each slice and keeps the largest2d component.

In Stage 1 e the largest 3d component is kept. In Stage 1 f the resultis dilated 5 mm on the axial plane, considering voxels with HU values inthe range 0-525. In Stage 1 g the result is eroded 2 mm on the axialplane and the largest 3d component is kept. In Stage 1 h, Stages 1 f and1 g are repeated nine more times each time with the result of theprevious iteration.

Reference is now made to FIG. 10c which is a flowchart of an exemplary,illustrative method for removal of bone fragments near the carotidarteries in the brain in a CT scan according to at least someembodiments of the present invention. In Stage 2 a the algorithm goesthrough the slices between the lower slice of the sinuses and the upperslice of the carotid siphon (as derived with reference to FIG. 6f ). Foreach slice a rectangle is marked bounded by the carotids and thevertebral/basilar arteries. The voxels inside the rectangle areconsidered as bone.

Reference is now made to FIG. 10d which is a flowchart of an exemplary,illustrative method for adding vessels to the brain in a CT scanaccording to at least some embodiments of the present invention.

In Stage 3 a the image is thresholded for HU values above 50 in theslice range of the brain segmentation (FIG. 2c ). In Stage 3 b a filteris applied for circles on the axial, sagittal and coronal planes witharea up to 20 mm² and 30% tolerance. In Stage 3 c the result is erodedby 1 voxel and voxels with Hounsfield value above 525 are removed. InStage 3 d blood vessel components with volume larger than 6.3 mm³ arekept.

In Stage 3 e the result is ballooned using fast marching until reachinga path cost of 10. A local cost of 1 is used for the HU range of 70-525.When outside this range, the local cost increase exponentially in therange of 20 Hounsfield.

${cost} = \left\{ \begin{matrix}{{\exp \left\lbrack {{- \left( {70 - {HU}} \right)}/20} \right\rbrack},} & {{HU} < 70} \\{1,} & {70 < {HU} < 525} \\{{\exp \left\lbrack {{- \left( {{HU} - 525} \right)}/20} \right\rbrack},} & {{HU} > 525}\end{matrix} \right.$

The local cost is multiplied by a vesselness measure. The vesselness isa measure score from 0 to 1 indicating how vessel-like a voxel is. It iscalculated from the Hessian. Marking x1, x2, and x3 as the eigenvaluesof the Hessian (x1>x2>x3) which is calculated with a Gaussian filterwith a scale of 4 mm.

Vesselness=Sigmoid(x1*0.2)*Sigmoid(x1*0.2)

Where Sigmoid is the standard sigmoid function as is known in the art.

In Stage 3 f, Stage 3 e is repeated. In Stage 3 g, Stage 3 e is repeatedbut the stopping criteria is changed at a total path cost of 40. Theresult is vessel segmentation inside the brain

Reference is now made to FIG. 11 which is an exemplary, illustrativescreenshot of a medical image viewed in a medical image viewer showingsegmented arteries of the head and neck according to at least someembodiments of the present invention. In FIG. 11, the segmented arteries1100 are highlighted and a drop-down list 1102 allows a radiologist tohighlight a specific segmented artery.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. The materials, methods, andexamples provided herein are illustrative only and not intended to belimiting.

Implementation of the method and system of the present inventioninvolves performing or completing certain selected tasks or stepsmanually, automatically, or a combination thereof. Moreover, accordingto actual instrumentation and equipment of preferred embodiments of themethod and system of the present invention, several selected steps couldbe implemented by hardware or by software on any operating system of anyfirmware or a combination thereof. For example, as hardware, selectedsteps of the invention could be implemented as a chip or a circuit. Assoftware, selected steps of the invention could be implemented as aplurality of software instructions being executed by a computer usingany suitable operating system. In any case, selected steps of the methodand system of the invention could be described as being performed by adata processor, such as a computing platform for executing a pluralityof instructions.

Although the present invention is described with regard to a “computer”on a “computer network”, it should be noted that optionally any devicefeaturing a data processor and the ability to execute one or moreinstructions may be described as a computer, including but not limitedto any type of personal computer (PC), a server, a cellular telephone,an IP telephone, a smart phone, a PDA (personal digital assistant), or apager. Any two or more of such devices in communication with each othermay optionally comprise a “computer network”.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made.

What is claimed is:
 1. A method for automated segmentation of a segmentof a blood vessel of a subject in a medical image comprising volumetricdata, comprising: a) segmenting circular components in parallel planesof the volumetric data; and b) for each of the circular components,identifying corresponding circular components from the circularcomponents in adjacent planes to define a contiguous segment ofcorresponding circular components spanning a plurality of the planes;wherein the contiguous segment defines a segment of a blood vessel. 2.The method of claim 1 wherein the corresponding circular components aredefined by a maximal overlap of the circular components.
 3. The methodof claim 1 wherein the corresponding circular components are defined bycloseness of the center of mass of the circular components.
 4. Themethod of claim 1 wherein the medical image is a CT scan and wherein thecircular components are identified from the intersection of a wide HUrange and a narrow HU range.
 5. A method for automated segmentation of ablood vessel of a head and neck of a subject in a medical image, themethod comprising: identifying the location of anatomical landmarks inthe medical image; identifying regions of interest in the medical imagebased on the landmarks; segmenting segments of blood vessels in themedical image; classifying at least one of the segments as defining theblood vessel based on its position relative to the landmarks within theregions of interest to create a classified blood vessel; identifying astarting seed for the blood vessel from the classified blood vessel;identifying an ending seed for the blood vessel from the classifiedblood vessel; segmenting the blood vessel between the starting seed andthe ending seed; and defining a path between the starting seed and theending seed.
 6. The method of claim 5, wherein the blood vesselcomprises the right internal and common carotid artery or the leftinternal and common carotid artery.
 7. The method of claim 5, whereinthe blood vessel comprises the right external carotid artery or the leftexternal carotid artery.
 8. The method of claim 5, wherein the bloodvessel comprises the right vertebral artery or the left vertebral arteryand wherein the region of interest comprises a segmented left and rightinternal carotid artery and segmented basilar artery.
 9. The method ofclaim 5, wherein the blood vessel comprises at least one of the basilarartery, the aortic arch, or a blood vessel in the brain.
 10. The methodof claim 5, wherein the image comprises a plurality of slices, furthercomprising: automatically identifying a plurality of landmark slicescontaining each of the anatomical landmarks in the medical image,wherein the landmarks comprise at least one of the lungs, trachea,brain, skull, or segmented blood vessels; automatically identifyingrelevant landmark slices for finding the blood vessel based on thepositional relationships of the landmarks and the blood vessel; andmanually identifying a starting seed for the blood vessel from within aset of slices that is constrained to the relevant landmark slices if theidentifying a starting seed fails.
 11. The method of claim 5, whereinthe landmarks comprise at least one of the lungs, trachea, brain, orskull.
 12. The method of claim 11, further comprising identifying theskull orientation.
 13. The method of claim 12, wherein the medical imagecomprises volumetric data.
 14. The method of claim 13, wherein thesegmenting segments of blood vessels comprises: segmenting circularcomponents in parallel planes of the volumetric data; and for each ofthe circular components, identifying corresponding circular componentsfrom the circular components in the adjacent planes to define acontiguous segment of corresponding circular components spanning aplurality of the planes; wherein the contiguous segment defines asegment of a blood vessel.
 15. The method of claim 14 wherein thecorresponding circular components are defined by a maximal overlap ofthe circular components.
 16. The method of claim 14 wherein thecorresponding circular components are defined by closeness of the centerof mass of the circular components.
 17. The method of claim 14 whereinthe medical image is a CT scan and wherein the circular components areidentified from the intersection of a wide HU range and a narrow HUrange.
 18. The method of claim 12, wherein the medical image comprises aplurality of slices.
 19. The method of claim 18, wherein the identifyingof the anatomical landmarks further comprises: initial segmentation ofthe lungs comprising locating the upper lung slice; initial segmentationof the brain comprising locating the brain base slice; segmentation ofthe skull comprising locating the skull base slice; and initialsegmentation the trachea further comprising determining the center ofthe neck on the axial plane, wherein the bottom of the trachea is in theupper lung slice.
 20. The method of claim 19, wherein the regions ofinterest comprise at least one of: the region between the upper lungslice and the brain base slice; the brain, the region above the skullbase slice, or the region between the upper lung slice and the skullbase slice.
 21. The method of claim 19, wherein the blood vessel isdefined as being on the right or the left of the subject based on atleast one of the center of the neck, the center of the image, or theskull orientation.
 22. The method of claim 8, wherein the medical imagecomprises slices and the region of interest is defined based on therelative position of the segmented carotid artery in each slice wherethe segmented carotid artery is located and in the slices where thebrain is segmented.
 23. The method of claim 5, wherein the image isobtained after injection of an intravenous radiocontrast agent into thesubject.
 24. A method for semi-automated identification of a seed forsegmentation of a blood vessel of a head and neck of a subject in amedical image wherein the image comprises a plurality of slices, themethod comprising: automatically identifying anatomical landmarks andthe plurality of landmark slices containing each of the anatomicallandmarks in the medical image; automatically identifying relevantlandmark slices for finding the blood vessel based on the positionalrelationships of the landmarks and the blood vessel; and manuallyidentifying the seed for the blood vessel from within a set of slicesthat is constrained to the relevant landmark slices.
 25. The method ofclaim 24, wherein the landmarks comprise at least one of the lungs,trachea, brain, skull, or segmented blood vessels.